{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import os\n",
    "import matplotlib \n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline\n",
    "import sys\n",
    "sys.path.append(\"../utils\")\n",
    "sys.path.append(\"../\")\n",
    "import utils.util_lookup_table\n",
    "import utils.util_data\n",
    "from pprint import pprint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "[f[:-4] for f in os.listdir(\"./results\")]\n",
    "def lighten_color(color, amount=0.5):\n",
    "    \"\"\"\n",
    "    Lightens the given color by multiplying (1-luminosity) by the given amount.\n",
    "    Input can be matplotlib color string, hex string, or RGB tuple.\n",
    "\n",
    "    Examples:\n",
    "    >> lighten_color('g', 0.3)\n",
    "    >> lighten_color('#F034A3', 0.6)\n",
    "    >> lighten_color((.3,.55,.1), 0.5)\n",
    "    \"\"\"\n",
    "    import matplotlib.colors as mc\n",
    "    import colorsys\n",
    "    try:\n",
    "        c = mc.cnames[color]\n",
    "    except:\n",
    "        c = color\n",
    "    c = colorsys.rgb_to_hls(*mc.to_rgb(c))\n",
    "    return colorsys.hls_to_rgb(c[0], 1 - amount * (1 - c[1]), c[2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "private_preamble QPSK_neural_vs_poly 8_4\n",
      "[14.599999999999913, 12.999999999999918, 11.999999999999922, 10.399999999999928, 8.399999999999935, 4.1999999999999496]\n",
      "[0.19382   0.1308825 0.12585   0.1249675 0.124945  0.12491  ]\n",
      "[1.12920e-01 1.31175e-02 2.16500e-03 2.82500e-04 4.75000e-05 2.50000e-06]\n",
      "[0.2027675 0.13364   0.126945  0.12577   0.125535  0.1256175]\n",
      "private_preamble QPSK_neural_vs_selfalien 8_4\n",
      "[14.599999999999913, 12.999999999999918, 11.999999999999922, 10.399999999999928, 8.399999999999935, 4.1999999999999496]\n",
      "[1.051675e-01 1.040000e-02 1.372500e-03 1.375000e-04 2.000000e-05\n",
      " 0.000000e+00]\n",
      "[1.02585e-01 9.46000e-03 1.16750e-03 1.07500e-04 1.00000e-05 0.00000e+00]\n",
      "[1.24355e-01 1.23300e-02 1.80000e-03 2.65000e-04 4.50000e-05 2.50000e-06]\n",
      "private_preamble QPSK_poly_vs_clone 8_4\n",
      "[14.599999999999913, 12.999999999999918, 11.999999999999922, 10.399999999999928, 8.399999999999935, 4.1999999999999496]\n",
      "[1.42045e-01 2.17750e-02 4.89750e-03 1.01000e-03 3.02500e-04 2.75000e-05]\n",
      "[1.122375e-01 1.247750e-02 1.817500e-03 2.200000e-04 4.250000e-05\n",
      " 0.000000e+00]\n",
      "[0.1901675 0.1310025 0.126005  0.1254325 0.1254625 0.125385 ]\n",
      "private_preamble QPSK_neural_vs_clone 8_4\n",
      "[14.599999999999913, 12.999999999999918, 11.999999999999922, 10.399999999999928, 8.399999999999935, 4.1999999999999496]\n",
      "[1.034775e-01 9.970000e-03 1.300000e-03 1.300000e-04 2.000000e-05\n",
      " 0.000000e+00]\n",
      "[1.0145e-01 9.1375e-03 1.0925e-03 8.5000e-05 1.0000e-05 0.0000e+00]\n",
      "[1.084275e-01 1.197500e-02 1.812500e-03 1.850000e-04 3.500000e-05\n",
      " 2.500000e-06]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/global/software/sl-7.x86_64/modules/langs/python/3.6/lib/python3.6/site-packages/matplotlib/axes/_base.py:1398: UserWarning: aspect is not supported for Axes with xscale=linear, yscale=log\n",
      "  'yscale=%s' % (xscale, yscale))\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b981572fef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "br = utils.util_lookup_table.BER_lookup_table()\n",
    "plt.style.use('seaborn-ticks')\n",
    "color = {\n",
    " 'QPSK_classic_and_neural':\"r\",\n",
    " 'QPSK_neural_vs_clone':\"r\",\n",
    " 'QPSK_neural_and_neural':\"g\",\n",
    " 'QPSK_neural_vs_selfalien':'g',\n",
    " 'QPSK_neural_and_classic':\"b\",\n",
    " 'QPSK_neural_vs_poly':'y',\n",
    " 'QPSK_neural_vs_classic':\"orange\",\n",
    " 'QPSK_poly_vs_clone':\"blue\",\n",
    "}\n",
    "\n",
    "LINE_STYLES = {\n",
    "               'neural_vs_poly': (0, (7, 3)),\n",
    "               'poly_vs_clone':(0, (1, 1)),\n",
    "               'neural_vs_clone':'solid',#(0, (2, 7)),\n",
    "               'neural_vs_selfalien':'solid',\n",
    "              }\n",
    "DARKEN_BY = {\n",
    "               'neural_vs_poly':1,\n",
    "               'poly_vs_clone':1,\n",
    "               'neural_vs_clone':1,\n",
    "               'neural_vs_selfalien':.5\n",
    "            }\n",
    "\n",
    "COLORS = {\n",
    "               'neural_vs_poly':'purple',\n",
    "               'poly_vs_clone':'r',\n",
    "               'neural_vs_clone':'b',\n",
    "               'neural_vs_selfalien':'b'\n",
    "            }\n",
    "\n",
    "fig,(ber, db3, db5) = plt.subplots(3, 1)\n",
    "fig.set_figheight(4.5*3)\n",
    "fig.set_figwidth(6)\n",
    "# for ax in axes:\n",
    "#     ax.set_aspect('equal', adjustable=\"datalim\")\n",
    "# (ber, db3, db5) = axes\n",
    "# fig.suptitle('Private w/ QPSK at 8.4dB Training SNR')\n",
    "baseline=False\n",
    "must_include = ['private', '8_4']\n",
    "must_exclude = ['classic']#['poly', 'selfalien']\n",
    "k=0\n",
    "for experiment_name in os.listdir(\"./results/\"):\n",
    "    if (not all([_ in experiment_name for _ in must_include])) \\\n",
    "        or any([_ in experiment_name for _ in must_exclude]):\n",
    "        continue\n",
    "    k+=1\n",
    "    f = os.path.join(\"./results\", experiment_name)\n",
    "    protocol, experiment_name, snr = experiment_name[:-4].split(\"-\")\n",
    "    models = \"_\".join(experiment_name.split(\"_\")[1:])\n",
    "    print(protocol, experiment_name, snr)\n",
    "    snr = float(snr.replace(\"_\",\".\"))\n",
    "    r = np.load(f, allow_pickle=True).item()    \n",
    "    fs_x = np.fliplr(np.array(r['test_SNR_dbs']).reshape((1, 6)))[0]\n",
    "    print(r['test_SNR_dbs'])\n",
    "    fs_mid = np.fliplr(np.array(r['BER_mid']).reshape((1, 6)))[0]\n",
    "    fs_low = np.fliplr(np.array(r['BER_low']).reshape((1, 6)))[0]\n",
    "    fs_high = np.fliplr(np.array(r['BER_high']).reshape((1, 6)))[0]\n",
    "    ckey = models\n",
    "    dkey= models\n",
    "    lkey= models\n",
    "    right = 600000\n",
    "    inset = True\n",
    "#     label = \", \".join([models, protocol]).replace(\"_\", \" \")\n",
    "    label=models.replace(\"_\", \" \").title().replace(\"Vs\", \"and\")\n",
    "    print(fs_mid)\n",
    "    print(fs_low)\n",
    "    print(fs_high)\n",
    "    ber.errorbar(\n",
    "                    np.array(fs_x) + k*1e-1, \n",
    "                    fs_mid,  \n",
    "                    yerr = [fs_mid - fs_low, fs_high- fs_mid], \n",
    "                    elinewidth = 2, \n",
    "                    linestyle = LINE_STYLES[lkey],\n",
    "                    color = lighten_color(COLORS[ckey], amount=DARKEN_BY[dkey]),\n",
    "                    #fmt = 'o-', \n",
    "                    label=label,\n",
    "                    linewidth = 3,\n",
    "                    alpha = .50 if LINE_STYLES[lkey] == \"solid\" else 1.0,\n",
    "                )\n",
    "    db3.plot([0.0]+r['symbols_sent']+[right], \n",
    "             np.concatenate(([0.0],r['3db_off'],r['3db_off'][-1:])),#'o-',\n",
    "                    linestyle = LINE_STYLES[lkey],\n",
    "                    color = lighten_color(COLORS[ckey], amount=DARKEN_BY[dkey]),\n",
    "                    label=label,\n",
    "                    linewidth = 3,\n",
    "                    alpha=(.50 if LINE_STYLES[lkey] == \"solid\" else 1.0),\n",
    "            )\n",
    "    db5.plot([0.0]+r['symbols_sent']+[right], \n",
    "             np.concatenate(([0.0],r['5db_off'],r['5db_off'][-1:])),#'o-',\\\n",
    "                    linestyle = LINE_STYLES[lkey],\n",
    "                    color = lighten_color(COLORS[ckey], amount=DARKEN_BY[dkey]),\n",
    "                    label=label,\n",
    "                    linewidth = 3,\n",
    "                    alpha=(.50 if LINE_STYLES[lkey] == \"solid\" else 1.0),\n",
    "            )\n",
    "    if not baseline:\n",
    "        ber_baseline = [br.get_optimal_BER_roundtrip(test_SNR_db, 2) \n",
    "                        for test_SNR_db in np.fliplr(np.array(r['test_SNR_dbs']).reshape((1, 6)))[0]]\n",
    "#         ber3_baseline = [br.get_optimal_BER_roundtrip(test_SNR_db-3, 2) \n",
    "#                         for test_SNR_db in np.fliplr(np.array(r['test_SNR_dbs']).reshape((1, 6)))[0]]\n",
    "#         ber5_baseline = [br.get_optimal_BER_roundtrip(test_SNR_db-5, 2) \n",
    "#                         for test_SNR_db in np.fliplr(np.array(r['test_SNR_dbs']).reshape((1, 6)))[0]]\n",
    "        ber.plot( np.fliplr(np.array(r['test_SNR_dbs']).reshape((1, 6)))[0], \n",
    "              ber_baseline,#'o-b',\n",
    "              label=\"QPSK Baseline\", color ='gray', linewidth = 3, alpha=1)\n",
    "#         ber.plot( np.fliplr(np.array(r['test_SNR_dbs']).reshape((1, 6)))[0], \n",
    "#               ber3_baseline,#'o-b',\n",
    "#               label=\"3db off\", color ='gray', linewidth = 3, alpha=.8)\n",
    "#         ber.plot( np.fliplr(np.array(r['test_SNR_dbs']).reshape((1, 6)))[0], \n",
    "#               ber5_baseline,\n",
    "#               label=\"5db off\", color ='gray', linewidth = 3, alpha=.5)\n",
    "        baseline=True\n",
    "ber.set_aspect('equal')\n",
    "ber.set_yscale('log')\n",
    "ber.set_title(\"Median Bit Error Curves\")\n",
    "ber.set_xlabel('SNR(dB)')\n",
    "ber.set_ylabel('Round-trip BER')\n",
    "# ber.set_xlim(right=14.0)\n",
    "# inset axes....\n",
    "# if inset:\n",
    "    \n",
    "# ber.legend()\n",
    "\n",
    "db3.set_ylim(top=1.1, bottom=-0.1)\n",
    "db5.set_ylim(top=1.1, bottom=-0.1)\n",
    "db3.set_xlim(right=right)\n",
    "db5.set_xlim(right=right)\n",
    "\n",
    "db3.set_title(\"Fraction of Trials within 3db\")\n",
    "db3.set_ylabel('Fraction of Trials Converged within 3dB')\n",
    "db3.set_xlabel('Number of preamble symbols transmitted')\n",
    "\n",
    "db5.set_title(\"Fraction of seeds within 5db\")\n",
    "db5.set_ylabel('Fraction of Trials Converged within 5dB')\n",
    "db5.set_xlabel('Number of preamble symbols transmitted')\n",
    "# db5.set_aspect('equal')\n",
    "handles, labels = ber.get_legend_handles_labels()\n",
    "order = sorted(range(len(labels)),key=labels.__getitem__)\n",
    "# order = [order[i] for i in [0,2,1,4,3,5]] \n",
    "# order = [order[i] for i in [0,2,1]] \n",
    "handles = [handles[idx] for idx in order]\n",
    "labels = [labels[idx] for idx in order]\n",
    "# Put a legend below current axis\n",
    "fig.legend(handles,                # The line objects\n",
    "           labels,\n",
    "           borderaxespad=0.1,    # Small spacing around legend box\n",
    "#            title=\"Protocols and Models\",  # Title for the legend\n",
    "           loc='lower center',\n",
    "           fancybox=True, shadow=True, ncol=1,\n",
    "           handlelength=5\n",
    "           )\n",
    "fig.tight_layout()\n",
    "# fig.subplots_adjust(top=0.85)\n",
    "fig.subplots_adjust(bottom=0.15, hspace = 0.4)  \n",
    "fig.savefig('./alien_tuned.pdf', format = 'pdf', bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{13: {'3db_off': array([0., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
       "         1., 1., 1., 1., 1., 1.]),\n",
       "  '5db_off': array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
       "         1., 1., 1., 1., 1., 1.]),\n",
       "  'BER_high': array([0.000e+00, 0.000e+00, 6.500e-05, 9.350e-04, 8.515e-03, 9.760e-02]),\n",
       "  'BER_low': array([0.000e+00, 0.000e+00, 6.500e-05, 9.350e-04, 8.515e-03, 9.760e-02]),\n",
       "  'BER_mid': array([0.000e+00, 0.000e+00, 6.500e-05, 9.350e-04, 8.515e-03, 9.760e-02]),\n",
       "  'SNR_db_off_for': 8.399999999999935,\n",
       "  'final_bers': array([[0.000e+00, 0.000e+00],\n",
       "         [0.000e+00, 0.000e+00],\n",
       "         [6.500e-05, 6.500e-05],\n",
       "         [9.350e-04, 9.350e-04],\n",
       "         [8.515e-03, 8.515e-03],\n",
       "         [9.760e-02, 9.760e-02]]),\n",
       "  'max_num_logs': 23,\n",
       "  'num_trials': 2,\n",
       "  'symbols_sent': [32,\n",
       "   256,\n",
       "   480,\n",
       "   704,\n",
       "   928,\n",
       "   1152,\n",
       "   1376,\n",
       "   1600,\n",
       "   1824,\n",
       "   2048,\n",
       "   2272,\n",
       "   2496,\n",
       "   2720,\n",
       "   2944,\n",
       "   3168,\n",
       "   3392,\n",
       "   3616,\n",
       "   3840,\n",
       "   4064,\n",
       "   4288,\n",
       "   4512,\n",
       "   4736,\n",
       "   4800],\n",
       "  'test_SNR_dbs': [14.599999999999913,\n",
       "   12.999999999999918,\n",
       "   11.999999999999922,\n",
       "   10.399999999999928,\n",
       "   8.399999999999935,\n",
       "   4.1999999999999496]}}"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from compile_results import process_experiment\n",
    "from pprint import pprint\n",
    "import numpy as np\n",
    "# import os\n",
    "# os.listdir(\"./\")\n",
    "# process_experiment(\"./\")\n",
    "meta, r1 = np.load('20190813_040500_0.npy')[0], np.load('20190813_040500_0.npy')[1:]\n",
    "r2 = np.load('20190813_040906_0.npy')[1:]\n",
    "nequal = False\n",
    "for i,j in zip(r1, r2):\n",
    "    if i.keys() != j.keys():\n",
    "        print(\"keys not eq \", i.keys(), j.keys())\n",
    "        break\n",
    "    for k in i.keys():\n",
    "        nequal =  i[k]!=j[k]\n",
    "        if isinstance(nequal, np.ndarray):\n",
    "            nequal = np.any(nequal)\n",
    "        if nequal:\n",
    "            pprint(i[k])\n",
    "            pprint(j[k])\n",
    "            break\n",
    "    if nequal:\n",
    "        break\n",
    "\n",
    "process_experiment(\"./\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys([13])\n",
      "dict_keys(['symbols_sent', 'BER_mid', 'BER_high', 'num_trials', 'final_bers', 'test_SNR_dbs', 'SNR_db_off_for', 'BER_low', '3db_off', '5db_off', 'max_num_logs'])\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/caryn/anaconda/envs/tensorflow/lib/python3.5/site-packages/matplotlib/axes/_base.py:1376: UserWarning: aspect is not supported for Axes with xscale=linear, yscale=log\n",
      "  'yscale=%s' % (xscale, yscale))\n"
     ]
    },
    {
     "data": {
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EAAAAIkIZSHDKBjrqrLPO0syZM9WnTx9JUmNjo55++ml98MEHAVcGZDhmSAAA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\nqbVr164FegNN6a5JkiRd6H8BO/JhIoV/PnYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x111632f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "br = utils.util_lookup_table.BER_lookup_table()\n",
    "color = {\n",
    " 'QPSK_neural_vs_clone':\"r\",\n",
    " 'QPSK_neural_vs_selfalien':'g',\n",
    " 'QPSK_neural_vs_poly':'b',\n",
    " 'QPSK_classic_and_neural':\"orange\",\n",
    " 'QPSK_poly_vs_clone':\"blue\",\n",
    "}\n",
    "fig,(ber, db3, db5) = plt.subplots(1, 3)\n",
    "fig.set_figheight(5)\n",
    "fig.set_figwidth(6*3)\n",
    "# for ax in axes:\n",
    "#     ax.set_aspect('equal', adjustable=\"datalim\")\n",
    "# (ber, db3, db5) = axes\n",
    "fig.suptitle('QPSK')\n",
    "baseline=False\n",
    "\n",
    "#     f = os.path.join(\"./results\", experiment_name)\n",
    "experiment_name = meta['experiment_name']\n",
    "experiment_dict = process_experiment(\"./\")\n",
    "print(experiment_dict.keys())\n",
    "mid_ber = sorted(experiment_dict.keys())[0]\n",
    "r = experiment_dict[mid_ber]\n",
    "print(r.keys())\n",
    "#     print(r['test_SNR_dbs'], np.fliplr(np.array(r['BER_mid']).reshape((1, 6))))\n",
    "# ber.plot( np.fliplr(np.array(r['test_SNR_dbs']).reshape((1, 6)))[0], \n",
    "#           np.fliplr(np.array(r['BER_mid']).reshape((1, 6)))[0],'o-b', \n",
    "#           label=experiment_name, color = color[experiment_name], linewidth = 3)\n",
    "db3.plot(r['symbols_sent'], r['3db_off'],'o-b',  label=experiment_name, color = color[experiment_name], linewidth = 3, alpha=.5)\n",
    "db5.plot(r['symbols_sent'], r['5db_off'], 'o-b',label=experiment_name, color = color[experiment_name], linewidth = 3, alpha=.5)\n",
    "if not baseline:\n",
    "    ber_baseline = [br.get_optimal_BER_roundtrip(test_SNR_db, 2) \n",
    "                    for test_SNR_db in np.fliplr(np.array(r['test_SNR_dbs']).reshape((1, 6)))[0]]\n",
    "    ber.plot( np.fliplr(np.array(r['test_SNR_dbs']).reshape((1, 6)))[0], \n",
    "          ber_baseline,'o-b',\n",
    "          label=\"baseline\", color ='gray', linewidth = 3)\n",
    "    baseline=True\n",
    "ber.set_aspect('equal')\n",
    "ber.set_yscale('log')\n",
    "# ber.legend()\n",
    "\n",
    "db3.set_ylim(top=1.1, bottom=-0.1)\n",
    "db5.set_ylim(top=1.1, bottom=-0.1)\n",
    "# db5.set_aspect('equal')\n",
    "handles, labels = ber.get_legend_handles_labels()\n",
    "\n",
    "\n",
    "\n",
    "# Put a legend below current axis\n",
    "fig.legend(handles,                # The line objects\n",
    "           labels,\n",
    "           borderaxespad=0.1,    # Small spacing around legend box\n",
    "           title=\"Legend Title\",  # Title for the legend\n",
    "           loc='lower center',\n",
    "           fancybox=True, shadow=True, ncol=5\n",
    "           )\n",
    "# fig.tight_layout()\n",
    "fig.subplots_adjust(bottom=0.2)  \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "ename": "ImportError",
     "evalue": "No module named 'models.modulators'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-55-bca07ccb99e8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mmodels\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodulators\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclassic\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mClassic\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mebnodb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpsk81\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mebnodb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mqpsk1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mImportError\u001b[0m: No module named 'models.modulators'"
     ]
    }
   ],
   "source": [
    "# plt.plot(ebnodb, psk81)\n",
    "# plt.plot(ebnodb, qpsk1)\n",
    "# plt.plot(ebnodb, qam161)\n",
    "# plt.plot(test_SNR_dbs, psk8)\n",
    "# plt.plot(test_SNR_dbs, qpsk)\n",
    "# plt.plot(test_SNR_dbs, qam16)\n",
    "# plt.xlabel(\"Eb/N0\")\n",
    "# plt.ylabel(\"BER\")\n",
    "# plt.yscale('log')\n",
    "# plt.ylim(10e-9, 10e-1)\n",
    "# plt.xlim(4, 16)\n",
    "# plt.show()\n",
    "# %BPSK BER\n",
    "# const=[1 -1];\n",
    "# size=100000;\n",
    "# iter_max=1000;\n",
    "# EbN0_min=0;\n",
    "# EbN0_max=10;\n",
    "# SNR=[];BER=[];\n",
    "# for EbN0 = EbN0_min:EbN0_max\n",
    "# EbN0_lin=10.^(0.1*EbN0);\n",
    "# noise_var=0.5/(EbN0_lin); % s^2=N0/2\n",
    "# iter = 0;\n",
    "# err = 0;\n",
    "# while (iter <iter_max && err <100),\n",
    "# bits=randsrc(1,size,[0 1]);\n",
    "# s=const(bits+1);\n",
    "# ;\n",
    "# bit_hat=(-sign(x)+1)/2;\n",
    "# err = err + sum(bits ~= bit_hat);\n",
    "# iter = iter + 1;\n",
    "# 5\n",
    "# end\n",
    "# SNR =[SNR EbN0];\n",
    "# BER = [BER err/(size*iter)];\n",
    "# end\n",
    "# semilogy(SNR,BER);grid;xlabel(’E_bN_0’);ylabel(’BER’);\n",
    "# title(’BPSK over AWGN channel’);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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r18bd3Z3hw4dz8eLFHB//yy+/ULlyZZYsWZLtPj8/PypXrkz16tWJjY3Ndn9k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\nhBBCSIf9P3YeyF1W3OIQAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x151d15cc88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plt.rcParams[\"font.family\"] = \"Times New Roman\"\n",
    "import matplotlib\n",
    "from matplotlib import pyplot as plt\n",
    "plt.style.use('seaborn-ticks')\n",
    "plt.rcParams.update({'xtick.labelsize' : 15, 'ytick.labelsize' : 15, 'axes.labelsize':15, 'legend.fontsize':15})\n",
    "# plt.rcParams[\"figure.figsize\"] = [16,10]\n",
    "matplotlib.rc('font', size=30)\n",
    "color = [0,0,\"red\",\"green\",\"blue\"]\n",
    "label = [0,0,\"QPSK\",\"8PSK\",\"QAM16\"]\n",
    "for bps in [2,3,4]:\n",
    "    test_SNR_dbs = list(utils.util_data.get_test_SNR_dbs()[bps]['ber_roundtrip'])\n",
    "    test_SNR_dbs.reverse()\n",
    "    ber_baseline = [br.get_optimal_BER_roundtrip(test_SNR_db, bps) \n",
    "         for test_SNR_db in np.array(test_SNR_dbs)]\n",
    "    plt.plot(test_SNR_dbs, ber_baseline,  marker='o',label=label[bps])\n",
    "plt.xlabel(\"SNR(dB)\")\n",
    "plt.ylabel(\"Round-trip BER\")\n",
    "plt.yscale('log')\n",
    "plt.legend()\n",
    "# plt.title(\"Round-trip BER Curves\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x151d10c0b8>"
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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aGrJ582YGDBjAlClTaNeuHQBff/01mzdvpnfv3ri4uBATE8OKFSuIjY1l8eLF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\niIioxlhAEBERUY2xgCAiIqIa+39tumHBes1vGAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x151eda3fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bps=2\n",
    "# plt.style.use('seaborn')\n",
    "test_SNR_dbs = list(utils.util_data.get_test_SNR_dbs()[bps]['ber_roundtrip'])\n",
    "test_SNR_dbs.reverse()\n",
    "ber_baseline = [br.get_optimal_BER_roundtrip(test_SNR_db, bps) for test_SNR_db in np.array(test_SNR_dbs)]\n",
    "# ber3_baseline = [br.get_optimal_BER_roundtrip(test_SNR_db-3, bps) for test_SNR_db in np.array(test_SNR_dbs)]\n",
    "# ber5_baseline = [br.get_optimal_BER_roundtrip(test_SNR_db-5, bps) for test_SNR_db in np.array(test_SNR_dbs)]\n",
    "plt.plot(test_SNR_dbs, ber_baseline,  marker='o',label='baseline')\n",
    "plt.scatter(8.4,br.get_optimal_BER_roundtrip(8.4-3, bps), color='red')\n",
    "plt.gca().annotate('test BER', xy=(8.4,br.get_optimal_BER_roundtrip(8.4-3, bps)), \n",
    "                   xytext=(0.8, 0.8), textcoords='axes fraction',\n",
    "                   horizontalalignment='right', verticalalignment='top',\n",
    "            arrowprops=dict(facecolor='black', shrink=0.05),fontsize =18\n",
    "            )\n",
    "plt.axvline(x=8.4, ymin=0, ymax=1, linestyle='dotted', color='gray')\n",
    "plt.axhline(y=0.0095, linestyle='dotted', color='gray')\n",
    "pair_x = (8.4,5.3)\n",
    "pair_y = (br.get_optimal_BER_roundtrip(8.4-3, bps), br.get_optimal_BER_roundtrip(8.4-3, bps))\n",
    "plt.plot(pair_x, pair_y, 'red', marker='o', linewidth=2)\n",
    "plt.gca().annotate('dB off', xy=(6.0,.1), textcoords='axes fraction', xytext=(.25,.92) ,fontsize =18, color='red')\n",
    "# plt.plot(test_SNR_dbs, ber3_baseline,label='3db off')\n",
    "# plt.plot(test_SNR_dbs, ber5_baseline,label='5db off')\n",
    "\n",
    "plt.xlabel(\"SNR(dB)\")\n",
    "plt.ylabel(\"Round-trip BER\")\n",
    "plt.yscale('log')\n",
    "plt.ylim(top=.2)\n",
    "plt.xlim(right=14.2)\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3.6",
   "language": "python",
   "name": "python3.6"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
